期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 28, 期 4, 页码 819-829出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2472477
关键词
Neuromorphic artificial touch; neuroprosthetics; neurorobotics; spike-based decoding; tactile sensing
类别
资金
- DG Connect through the NEBIAS European Project entitled NEurocontrolled BIdirectional Artificial Upper Limb and Hand Prosthesis [EU-FP7-ICT-611687]
- Italian Ministry of Foreign Affairs and International Cooperation
- Ministero dell'Istruzione [CUP: B81J12002680008, 20102YF2RY]
- European Commission through the NanoBioTouch European Project entitled Nanoresolved Multiscale Investigations of Human Tactile Sensations and Tissue Engineered Nanobiosensors [EU-FP7-NMP-228844]
We implemented neuromorphic artificial touch and emulated the firing behavior of mechanoreceptors by injecting the raw outputs of a biomimetic tactile sensor into an Izhikevich neuronal model. Naturalistic textures were evaluated with a passive touch protocol. The resulting neuromorphic spike trains were able to classify ten naturalistic textures ranging from textiles to glass to BioSkin, with accuracy as high as 97%. Remarkably, rather than on firing rate features calculated over the stimulation window, the highest achieved decoding performance was based on the precise spike timing of the neuromorphic output as captured by Victor Purpura distance. We also systematically varied the sliding velocity and the contact force to investigate the role of sensing conditions in categorizing the stimuli via the artificial sensory system. We found that the decoding performance based on the timing of neuromorphic spike events was robust for a broad range of sensing conditions. Being able to categorize naturalistic textures in different sensing conditions, these neurorobotic results pave the way to the use of neuromorphic tactile sensors in future real-life neuroprosthetic applications.
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